Over the previous two decades, a diverse array of geochemical negative emissions technologies (NETs) have been proposed, which use alkaline minerals for removing and permanently storing atmospheric carbon dioxide (CO2). Geochemical NETs include CO2 mineralization (methods which react alkaline minerals with CO2, producing solid carbonate minerals), enhanced weathering (dispersing alkaline minerals in the environment for CO2 drawdown) and ocean alkalinity enhancement (manipulation of ocean chemistry to remove CO2 from air as dissolved inorganic carbon). CO2 mineralization approaches include in situ (CO2 reacts with alkaline minerals in the Earth's subsurface), surficial (high surface area alkaline minerals found at the Earth's surface are reacted with air or CO2-bearing fluids), and ex situ (high surface area alkaline minerals are transported to sites of concentrated CO2 production). Geochemical NETS may also include an approach to direct air capture (DAC) that harnesses surficial mineralization reactions to remove CO2 from air, and produce concentrated CO2. Overall, these technologies are at an early stage of development with just a few subjected to field trials. In Part I of this work we have reviewed the current state of geochemical NETs, highlighting key features (mineral resources; processes; kinetics; storage durability; synergies with other NETs such as DAC, risks; limitations; co-benefits, environmental impacts and life-cycle assessment). The role of organisms and biological mechanisms in enhancing geochemical NETs is also explored. In Part II, a roadmap is presented to help catalyze the research, development, and deployment of geochemical NETs at the gigaton scale over the coming decades.

Geochemical negative emissions technologies (NETs) comprise a set of approaches to climate change mitigation that make use of alkaline minerals to remove and/or permanently store carbon dioxide (CO2) as solid carbonate minerals or dissolved ocean bicarbonate ions. This roadmap accompanies the comprehensive review of geochemical NETs by the same authors and offers guidance for the development and deployment of geochemical NETs at gigaton per year (Gt yr.−1) scale. We lay out needs and high-priority initiatives across six key elements required for the responsible and effective deployment of geochemical NETs: (i) technical readiness, (ii) social license, (iii) demand, (iv) supply chains, (v) human capital, and (vi) infrastructure. We put forward proposals for: specific initiatives to be undertaken; their approximate costs and timelines; and the roles that various actors could play in undertaking them. Our intent is to progress toward a working consensus among researchers, practitioners, and key players about initiatives that merit resourcing and action, primarily focusing on the near-term.

X-ray diffraction imaging (XRDI) offers the potential for reduced false alarm rates, increased throughputs, and more sensitive explosives detection performance in aviation security applications. The deployment of computed tomography (CT) systems across carry-on and checked baggage screening lanes has both reinforced the need for orthogonal detection technologies and created an exciting new opportunity for the implementation of XRDI. Our team at Quadridox built a novel XRDI system that, when combined with a CT system, realizes full-tunnel assessment of checked bags at a belt speed of 20 cm/s. We integrated our XRDI system with a Smiths CTX 5800 explosives detection system (EDS) and collected bag data containing both benign and threat objects. We describe the XRDI system, show examples of the resulting hybrid CT and XRD dataset, and present performance results for the hybrid system.

X-ray coherent diffraction-based measurements can provide more specific material information that is complementary to transmission-based material information. With increasing capability of the X-ray coherent diffraction-based systems and recent development of dual modality of X-ray transmission-based and diffraction-based systems, there is a significant potential for improving the overall system threat detection performance for material discrimination. Dual modality systems can yield higher detection probability (Pd) while lowering the probability of false alarm (Pfa), relative to the transmission modality. In this work, we analyze the material discrimination performance for two different machine learning classifiers: support vector machines (SVM) and neural networks (NN), using both simulation and experimental data obtained with a dual-modality X-ray system. Using simulation studies, we demonstrate significant improvement in material discrimination performance afforded by additional complementary information by coherent diffraction for a variety of materials. We further validate these improvements using an experimental dataset collected using real-world objects and materials.